Case Study: Machine Learning applied for ICOs Success, by Santiago Márquez Solís, CTO at Barrabés.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.
The Italian business graph: fueling innovation in financeCerved Group SpA
Cerved is an Italian data company that collects and analyzes complex data from over 50 sources to provide credit risk information, marketing solutions, and credit management services. Cerved began developing a graph database in 2011 to better analyze relationships between entities like companies, people, locations, and properties. Their graph platform now contains over 35 million nodes and 70 million relationships and is used for customer solutions, internal applications, and machine learning projects that apply graph algorithms to large datasets. Moving forward, Cerved aims to support more customer use cases and integrate additional customer data while also using their graph for internal search, feature creation, and machine learning applications.
Cerved is an Italian data and analytics company. It uses graph databases and network analysis to better understand complex relationships within data from over 50 sources. Cerved started using graph databases in 2011 to improve an algorithm, and has expanded usage to better link corporate data and power solutions through visualization and machine learning algorithms on graph data. Graph databases allow flexible and connected modeling of Cerved's data, and native graph storage and processing improves querying, integration, and analysis compared to relational databases.
GraphTour - Next generation solutions using Neo4jNeo4j
The document discusses how Neo4j can be used to build next generation fraud detection solutions. It outlines the limitations of traditional fraud detection approaches and how Neo4j enables connected analysis to detect complex fraud patterns across unlimited hops. Examples of using Neo4j for fraud detection at a large payments company are provided. The document also provides an example architecture of a Neo4j powered fraud solution.
TCP1P.net Meetup Vision, Objectives and RoadmapStefan Ianta
Toronto Code Pile 1 Programming Meetup - the social innovation network - How to build a platform for reactive microservices on a Nobel Prize Reverse Game Algorithm - 2017 Roadmap
GraphTour - Mastering highly distributed architecture with Neo4jNeo4j
This document discusses Amadeus, a leading technology company in the global travel industry, and its use of Neo4j and a tool called YAC to create a knowledge graph. It describes how YAC started with a rigid data model but evolved to use Neo4j as its database in order to handle increasing amounts of data, provide more flexibility and visualization. It discusses growing the knowledge graph to include more content from various sources and adjusting it for compliance with GDPR.
AI is Coming! Are You Ready? The story of “Self-Driving Datacenter”Sergey A. Razin
This document discusses machine learning and how it can be applied to optimize IT environments. It introduces SIOS Technology Corp and their product called SIOS iQ, which uses machine learning and topological data analysis to analyze VMware environments. The document explains how machine learning and algorithms can help address challenges of analyzing large and complex data in IT operations more effectively than conventional tools. It encourages readers to learn about machine learning through online courses to stay relevant in their fields.
The document discusses a proposed service search engine platform called World Servi.ca that would automate the global services ecosystem. It aims to solve large problems by decomposing them into microservices implemented through code. The platform would use a genetic algorithm to rank services and solutions, allowing clients like project managers to find optimal solutions. It is presented as having the potential to generate $1-10 trillion in economic activity by connecting various participants in the services market through an automated trading platform.
Case Study: Machine Learning applied for ICOs Success, by Santiago Márquez Solís, CTO at Barrabés.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.
The Italian business graph: fueling innovation in financeCerved Group SpA
Cerved is an Italian data company that collects and analyzes complex data from over 50 sources to provide credit risk information, marketing solutions, and credit management services. Cerved began developing a graph database in 2011 to better analyze relationships between entities like companies, people, locations, and properties. Their graph platform now contains over 35 million nodes and 70 million relationships and is used for customer solutions, internal applications, and machine learning projects that apply graph algorithms to large datasets. Moving forward, Cerved aims to support more customer use cases and integrate additional customer data while also using their graph for internal search, feature creation, and machine learning applications.
Cerved is an Italian data and analytics company. It uses graph databases and network analysis to better understand complex relationships within data from over 50 sources. Cerved started using graph databases in 2011 to improve an algorithm, and has expanded usage to better link corporate data and power solutions through visualization and machine learning algorithms on graph data. Graph databases allow flexible and connected modeling of Cerved's data, and native graph storage and processing improves querying, integration, and analysis compared to relational databases.
GraphTour - Next generation solutions using Neo4jNeo4j
The document discusses how Neo4j can be used to build next generation fraud detection solutions. It outlines the limitations of traditional fraud detection approaches and how Neo4j enables connected analysis to detect complex fraud patterns across unlimited hops. Examples of using Neo4j for fraud detection at a large payments company are provided. The document also provides an example architecture of a Neo4j powered fraud solution.
TCP1P.net Meetup Vision, Objectives and RoadmapStefan Ianta
Toronto Code Pile 1 Programming Meetup - the social innovation network - How to build a platform for reactive microservices on a Nobel Prize Reverse Game Algorithm - 2017 Roadmap
GraphTour - Mastering highly distributed architecture with Neo4jNeo4j
This document discusses Amadeus, a leading technology company in the global travel industry, and its use of Neo4j and a tool called YAC to create a knowledge graph. It describes how YAC started with a rigid data model but evolved to use Neo4j as its database in order to handle increasing amounts of data, provide more flexibility and visualization. It discusses growing the knowledge graph to include more content from various sources and adjusting it for compliance with GDPR.
AI is Coming! Are You Ready? The story of “Self-Driving Datacenter”Sergey A. Razin
This document discusses machine learning and how it can be applied to optimize IT environments. It introduces SIOS Technology Corp and their product called SIOS iQ, which uses machine learning and topological data analysis to analyze VMware environments. The document explains how machine learning and algorithms can help address challenges of analyzing large and complex data in IT operations more effectively than conventional tools. It encourages readers to learn about machine learning through online courses to stay relevant in their fields.
The document discusses a proposed service search engine platform called World Servi.ca that would automate the global services ecosystem. It aims to solve large problems by decomposing them into microservices implemented through code. The platform would use a genetic algorithm to rank services and solutions, allowing clients like project managers to find optimal solutions. It is presented as having the potential to generate $1-10 trillion in economic activity by connecting various participants in the services market through an automated trading platform.
Case Study: Machine Learning applied for ICOs Success, by Santiago Márquez Solís, CTO at Barrabés.
VSSML18: 4th edition of the Valencian Summer School in Machine Learning.
This document discusses trust in interactions with cognitive assistants. It begins by defining cognitive assistants as new decision tools that can augment human capabilities by understanding our environment with depth and clarity. Cognitive assistants can provide high-quality recommendations to help people make better data-driven decisions, and significantly augment people's problem-solving abilities through interaction. The document then discusses components of trust from different academic disciplines, such as ability, benevolence, integrity, predictability, and shared values. It poses questions about what jobs will remain for humans and ethical issues regarding situations like domestic violence. The document conjectures that AI combined with other information sources could surpass average professionals in some areas. It also speculates that societies of AI may form to optimize tasks in
The document discusses how technology is increasingly performing work tasks through digital workers, freeing up opportunities for people. It suggests educational technology could help people realize those opportunities. The document outlines how costs of computing are decreasing exponentially, and how AI and machine learning have advanced through deep learning techniques applied to large datasets. It envisions a future where cognitive systems/mediators could take online courses and coach students, with tools enabling much faster development of such systems. Overall, the document presents an optimistic view of how educational technology and cognitive systems could help improve learning and opportunities.
SnapLogic Academy India Launch
The document outlines the agenda for the SnapLogic Academy India Launch event. It includes details about the event location in Chennai, speakers and their topics. Some of the key speakers include Anish Raju who will discuss the evolution of the software industry in India. Karthik Sirasanagandla will discuss the trends of no-code and AI. Ganesh Mekala will provide an overview of SnapLogic iPaaS. There will also be a panel discussion on career opportunities and skills needed for jobs in the ETL and iPaaS space. The event aims to provide guidance to job seekers on preparing for careers with SnapLogic iPaaS.
IBM has been working on AI for decades, with early pioneers like Nathan Rochester. Currently, IBM is focusing on making AI more accessible through open source projects like CODAIT and Model Asset eXchange. IBM contributes to many open source projects related to AI and machine learning like Apache Spark. The future of AI involves continuing to build better basic building blocks for tasks like perception, reasoning and social skills. Ensuring AI is developed responsibly to benefit humanity is important as the technology progresses.
Neotys organized its first Performance Advisory Council in Scotland, the 14th & 15th of November.
With 15 Load Testing experts from several countries (UK, France, New-Zeland, Germany, USA, Australia, India…) we explored several theme around Load Testing such as DevOps, Shift Right, AI etc.
By discussing around their experience, the methods they used, their data analysis and their interpretation, we created a lot of high-value added content that you can use to discover what will be the future of Load Testing.
You want to know more about this event ? https://www.neotys.com/performance-advisory-council
1) Dr. Maurizio Pilu discusses his experience managing a £9m innovation program on the Internet of Things (IoT) at the UK's Technology Strategy Board (TSB).
2) He then explains his new role leading partnerships at the Connected Digital Economy Catapult, a £10m per year applied research center established by the TSB to help UK innovators in the digital economy.
3) The Catapult focuses on making specific capabilities and platforms openly available to benefit UK innovators working in areas like cities and communities, data, and next generation infrastructure relevant to IoT like machine-to-machine communication and whitespace networks.
The document summarizes a tutorial on Opentech AI given by Jim Spohrer and Daniel Pakkala, discussing trends in lowering the cost of AI technologies, benchmarks for measuring AI progress, and types of cognitive systems ranging from tools to mediators. It also provides an outline for Daniel Pakkala's presentation on the Opentech AI architecture, ecosystem, and roadmap, discussing frameworks for understanding intelligence evolution and the need for an architecture framework for AI systems.
1st BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
Navigating the Tricky Part towards future PLM innovation Oleg Shilovitsky
Slide deck from my PI PLMx event in Hamburg, 20 Feb 2018. Describes my perspective on innovation in engineering and manufacturing including PLM, PDM, CAD and cloud technologies
In this talk for the University of Glasgow's Future Proof IT event I explore a few near future careers and technologies that will impact learners and institutions, such as self-driving vehicles, and how we might respond to them.
The document discusses progress being made in artificial intelligence, including timelines for when AI may reach human-level capabilities in different areas, who the leaders are in driving AI progress, and how individuals and organizations can prepare for the future of AI by learning skills like programming and participating in online AI challenges and leaderboards.
Big Data, IoT and The Third Industrial Revolutionglobexspain
This document provides an overview of a session on emerging business tools for a new social and economic order in the context of the third industrial revolution. It discusses how exponential technologies like the Internet of Things, big data analytics, and artificial intelligence will transform business, society, and the self by redefining the work framework. It notes that human labor is ending and these tools offer huge emerging opportunities and threats. The document outlines several talks that will explore designing a new economic and social order, the importance of data science skills, and new business opportunities in areas like personalized medicine, security, and life logging through connected sensors.
This document outlines the agenda for the Chicago MuleSoft Meetup on September 18, 2019. The agenda includes introductions from Redwood Logistics and Big Compass, a presentation on Redwood's logging story and practices, a demo of logging with Log4j2 and ELK, and a discussion of Redwood's future vision which includes becoming a more data-driven organization.
2nd BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
This document summarizes a presentation about the future of AI and Fabric for Deep Learning (FfDL). It discusses how deep learning has advanced due to increased data and computing power, but that commonsense reasoning will require more research. FfDL is introduced as an open source project that aims to make deep learning accessible and scalable across frameworks. It uses a microservices architecture on Kubernetes to manage training jobs efficiently. Research is ongoing to further develop explainable and robust AI capabilities.
Jim Spohrer, director of IBM Cognitive OpenTech, discusses AI at IBM including its past, present, and future. Some key points include:
- IBM made early contributions to AI through projects like Deep Blue (chess-playing computer) and Watson (Jeopardy-playing computer).
- The present state of AI is focused on deep learning for pattern recognition tasks due to available data and computing power.
- The future of AI will require capabilities beyond deep learning like commonsense reasoning, which will take additional research over the next 5-10 years.
- IBM is working on technologies like quantum computing and blockchain to advance AI and tackle challenges like explainability, security, and ethics.
- Open source projects and
The document discusses the Internet of Things (IoT). It defines IoT as the network of physical objects embedded with sensors and connectivity that allows them to exchange data. IoT is changing both business and personal worlds by connecting physical objects to digital representations. Examples discussed include smart blankets, locks and baby monitors. The technology behind IoT includes sensors, cloud platforms and data analytics. Considerations around IoT focus on security, privacy, and limitations regarding power, software updates and data management. The document suggests that while still in its infancy, IoT offers many opportunities for innovative solutions across industries.
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
This document discusses trust in interactions with cognitive assistants. It begins by defining cognitive assistants as new decision tools that can augment human capabilities by understanding our environment with depth and clarity. Cognitive assistants can provide high-quality recommendations to help people make better data-driven decisions, and significantly augment people's problem-solving abilities through interaction. The document then discusses components of trust from different academic disciplines, such as ability, benevolence, integrity, predictability, and shared values. It poses questions about what jobs will remain for humans and ethical issues regarding situations like domestic violence. The document conjectures that AI combined with other information sources could surpass average professionals in some areas. It also speculates that societies of AI may form to optimize tasks in
The document discusses how technology is increasingly performing work tasks through digital workers, freeing up opportunities for people. It suggests educational technology could help people realize those opportunities. The document outlines how costs of computing are decreasing exponentially, and how AI and machine learning have advanced through deep learning techniques applied to large datasets. It envisions a future where cognitive systems/mediators could take online courses and coach students, with tools enabling much faster development of such systems. Overall, the document presents an optimistic view of how educational technology and cognitive systems could help improve learning and opportunities.
SnapLogic Academy India Launch
The document outlines the agenda for the SnapLogic Academy India Launch event. It includes details about the event location in Chennai, speakers and their topics. Some of the key speakers include Anish Raju who will discuss the evolution of the software industry in India. Karthik Sirasanagandla will discuss the trends of no-code and AI. Ganesh Mekala will provide an overview of SnapLogic iPaaS. There will also be a panel discussion on career opportunities and skills needed for jobs in the ETL and iPaaS space. The event aims to provide guidance to job seekers on preparing for careers with SnapLogic iPaaS.
IBM has been working on AI for decades, with early pioneers like Nathan Rochester. Currently, IBM is focusing on making AI more accessible through open source projects like CODAIT and Model Asset eXchange. IBM contributes to many open source projects related to AI and machine learning like Apache Spark. The future of AI involves continuing to build better basic building blocks for tasks like perception, reasoning and social skills. Ensuring AI is developed responsibly to benefit humanity is important as the technology progresses.
Neotys organized its first Performance Advisory Council in Scotland, the 14th & 15th of November.
With 15 Load Testing experts from several countries (UK, France, New-Zeland, Germany, USA, Australia, India…) we explored several theme around Load Testing such as DevOps, Shift Right, AI etc.
By discussing around their experience, the methods they used, their data analysis and their interpretation, we created a lot of high-value added content that you can use to discover what will be the future of Load Testing.
You want to know more about this event ? https://www.neotys.com/performance-advisory-council
1) Dr. Maurizio Pilu discusses his experience managing a £9m innovation program on the Internet of Things (IoT) at the UK's Technology Strategy Board (TSB).
2) He then explains his new role leading partnerships at the Connected Digital Economy Catapult, a £10m per year applied research center established by the TSB to help UK innovators in the digital economy.
3) The Catapult focuses on making specific capabilities and platforms openly available to benefit UK innovators working in areas like cities and communities, data, and next generation infrastructure relevant to IoT like machine-to-machine communication and whitespace networks.
The document summarizes a tutorial on Opentech AI given by Jim Spohrer and Daniel Pakkala, discussing trends in lowering the cost of AI technologies, benchmarks for measuring AI progress, and types of cognitive systems ranging from tools to mediators. It also provides an outline for Daniel Pakkala's presentation on the Opentech AI architecture, ecosystem, and roadmap, discussing frameworks for understanding intelligence evolution and the need for an architecture framework for AI systems.
1st BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
Navigating the Tricky Part towards future PLM innovation Oleg Shilovitsky
Slide deck from my PI PLMx event in Hamburg, 20 Feb 2018. Describes my perspective on innovation in engineering and manufacturing including PLM, PDM, CAD and cloud technologies
In this talk for the University of Glasgow's Future Proof IT event I explore a few near future careers and technologies that will impact learners and institutions, such as self-driving vehicles, and how we might respond to them.
The document discusses progress being made in artificial intelligence, including timelines for when AI may reach human-level capabilities in different areas, who the leaders are in driving AI progress, and how individuals and organizations can prepare for the future of AI by learning skills like programming and participating in online AI challenges and leaderboards.
Big Data, IoT and The Third Industrial Revolutionglobexspain
This document provides an overview of a session on emerging business tools for a new social and economic order in the context of the third industrial revolution. It discusses how exponential technologies like the Internet of Things, big data analytics, and artificial intelligence will transform business, society, and the self by redefining the work framework. It notes that human labor is ending and these tools offer huge emerging opportunities and threats. The document outlines several talks that will explore designing a new economic and social order, the importance of data science skills, and new business opportunities in areas like personalized medicine, security, and life logging through connected sensors.
This document outlines the agenda for the Chicago MuleSoft Meetup on September 18, 2019. The agenda includes introductions from Redwood Logistics and Big Compass, a presentation on Redwood's logging story and practices, a demo of logging with Log4j2 and ELK, and a discussion of Redwood's future vision which includes becoming a more data-driven organization.
2nd BIG IoT Webinar
The webinar will provide a general overview of the BIG IoT project, the technical solution and the application form of the 1st Open call.
This document summarizes a presentation about the future of AI and Fabric for Deep Learning (FfDL). It discusses how deep learning has advanced due to increased data and computing power, but that commonsense reasoning will require more research. FfDL is introduced as an open source project that aims to make deep learning accessible and scalable across frameworks. It uses a microservices architecture on Kubernetes to manage training jobs efficiently. Research is ongoing to further develop explainable and robust AI capabilities.
Jim Spohrer, director of IBM Cognitive OpenTech, discusses AI at IBM including its past, present, and future. Some key points include:
- IBM made early contributions to AI through projects like Deep Blue (chess-playing computer) and Watson (Jeopardy-playing computer).
- The present state of AI is focused on deep learning for pattern recognition tasks due to available data and computing power.
- The future of AI will require capabilities beyond deep learning like commonsense reasoning, which will take additional research over the next 5-10 years.
- IBM is working on technologies like quantum computing and blockchain to advance AI and tackle challenges like explainability, security, and ethics.
- Open source projects and
The document discusses the Internet of Things (IoT). It defines IoT as the network of physical objects embedded with sensors and connectivity that allows them to exchange data. IoT is changing both business and personal worlds by connecting physical objects to digital representations. Examples discussed include smart blankets, locks and baby monitors. The technology behind IoT includes sensors, cloud platforms and data analytics. Considerations around IoT focus on security, privacy, and limitations regarding power, software updates and data management. The document suggests that while still in its infancy, IoT offers many opportunities for innovative solutions across industries.
Similar to VSSML18. Machine Learning for ICOs (20)
Digital Transformation and Process Optimization in ManufacturingBigML, Inc
Keyanoush Razavidinani, Digital Services Consultant at A1 Digital, a BigML Partner, highlights why it is important to identify and reduce human bottlenecks that optimize processes and let you focus on important activities. Additionally, Guillem Vidal, Machine Learning Engineer at BigML completes the session by showcasing how Machine Learning is put to use in the manufacturing industry with a use case to detect factory failures.
The Road to Production: Automating your Anomaly Detectors - by jao (Jose A. Ortega), Co-Founder and Chief Technology Officer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML for AML ComplianceBigML, Inc
Machine Learning for Anti Money Laundering Compliance, by Kevin Nagel, Consultant and Data Scientist at INFORM.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - My First Anomaly Detector BigML, Inc
The document discusses building an anomaly detector model to identify unusual transactions in a dataset. It describes loading transaction data with 31 features into the BigML platform and creating an anomaly detector model. The model scores new data and identifies the most anomalous fields to help detect fraud. Creating the anomaly detector involves interpreting the data, exploring the dataset distribution, and setting a threshold score to define what is considered anomalous.
DutchMLSchool 2022 - History and Developments in MLBigML, Inc
History and Present Developments in Machine Learning, by Tom Dietterich, Emeritus Professor of computer science at Oregon State University and Chief Scientist at BigML.
*Machine Learning School in The Netherlands 2022.
Introduction to End-to-End Machine Learning: Classification and Regression - Mercè Martín, VP of Bindings and Applications at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - A Data-Driven CompanyBigML, Inc
A Data-Driven Company: 21 Lessons for Large Organizations to Create Value from AI, by Richard Benjamins, Chief AI and Data Strategist at Telefónica.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - ML in the Legal SectorBigML, Inc
How Machine Learning Transforms and Automates Legal Services, by Arnoud Engelfriet, Co-Founder at Lynn Legal.
*Machine Learning School in The Netherlands 2022.
This document describes a proposed solution using machine learning and artificial intelligence to help create a safer stadium experience. The solution involves two parts: 1) linking access to stadiums to a verified identity through a fan app for preregistration, and 2) using AI/ML to help detect unwanted behaviors or events early. The rest of the document provides more details on the proposed smart video review framework, including using computer vision and audio analysis techniques to help identify issues like flares, flags, banners, chants including monkey chants. The goal is to help reviewers more efficiently identify potential problems but with privacy, ethics and human oversight.
DutchMLSchool 2022 - Process Optimization in Manufacturing PlantsBigML, Inc
Process Optimization in Manufacturing Plants, by Keyanoush Razavidinani, Digital Business Consultant at A1 Digital.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Anomaly Detection at ScaleBigML, Inc
Lessons Learned Applying Anomaly Detection at Scale, by Álvaro Clemente, Machine Learning Engineer at BigML.
*Machine Learning School in The Netherlands 2022.
DutchMLSchool 2022 - Citizen Development in AIBigML, Inc
The document discusses the need for citizen developers and humans in the AI/ML process. It notes that while technology and talent are important, company culture must also support broad data analytics and AI/ML adoption. It then provides examples of how involving domain experts can help attribute meaning to correlations and build better causal models to improve AI systems. The document advocates for a systems thinking approach and having humans in the loop to help AI/ML systems consider the wider context and avoid issues like bias.
This new feature is a continuation of and improvement on our previous Image Processing release. Now, Object Detection lets you go a step further with your image data and allows you to locate objects and annotate regions in your images. Once your image regions are defined, you can train and evaluate Object Detection models, make predictions with them, and automate end-to-end Machine Learning workflows on a single platform. To make that possible, BigML enables Object Detection by introducing the regions optype.
As with any other BigML feature, Object Detection is available from the BigML Dashboard, API, and WhizzML for automation. Object Detection is extremely helpful to tackle a wide range of computer vision use cases such as medical image analysis, quality control in manufacturing, license plate recognition in transportation, people detection in security surveillance, among many others.
This new release brings Image Processing to the BigML platform, a feature that enhances our offering to solve image data-driven business problems with remarkable ease of use. Because BigML treats images as any other data type, this unique implementation allows you to easily use image data alongside text, categorical, numeric, date-time, and items data types as input to create any Machine Learning model available in our platform, both supervised and unsupervised.
Now, it is easier than ever to solve a wide variety of computer vision and image classification use cases in a single platform: label your image data, train and evaluate your models, make predictions, and automate your end-to-end Machine Learning workflows. As with any other BigML feature, Image Processing is available from the BigML Dashboard, API, and WhizzML, and it can be applied to solve use cases such as medical image analysis, visual product search, security surveillance, and vehicle damage detection, among others.
Machine Learning in Retail: Know Your Customers' Customer. See Your FutureBigML, Inc
This session presents a quite common situation for those working in food and beverage retail (FnB) and highlights interesting insights to fight waste reduction.
Speaker: Stephen Kinns, CEO and Co-Founder at catsAi.
*ML in Retail 2021: Webinar.
Machine Learning in Retail: ML in the Retail SectorBigML, Inc
This is an introductory session about the role that Machine Learning is playing in the retail sector and how it is being deployed across the different areas of this industry.
Speaker: Atakan Cetinsoy, VP of Predictive Applications at BigML.
*ML in Retail 2021: Webinar.
ML in GRC: Machine Learning in Legal Automation, How to Trust a LawyerbotBigML, Inc
This presentation analyzes the role that Machine Learning plays in legal automation with a real-world Machine Learning application.
Speaker: Arnoud Engelfriet, Co-Founder at Lynn Legal.
*ML in GRC 2021: Virtual Conference.
ML in GRC: Supporting Human Decision Making for Regulatory Adherence with Mac...BigML, Inc
This is a real-life Machine Learning use case about integrated risk.
Speakers: Thomas Rengersen, Product Owner of the Governance Risk and Compliance Tool for Rabobank, and Thomas Alderse Baas, Co-Founder and Director of The Bowmen Group.
*ML in GRC 2021: Virtual Conference.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
7. BigML, Inc 7
Clluc’s Projects
We have
already applied
Blockchain to
resolve some
problems
Petrolcontrol
SolidGO!
VALSAL
Kiitos
Managment Fee
GreenT
CPPB
Osiris
8. BigML, Inc 8VSSML18: PITIA PROJECT.
Why our focus is here? (1/2)
World is changing faster than ever before
9. BigML, Inc 9VSSML18: PITIA PROJECT.
Why our focus is here? (2/2)
Think about it. Are you a 2.0 or a 3.0 user?
10. BigML, Inc 10VSSML18: PITIA PROJECT.
So, we must find any others areas for working, because be
different is part of our vision
But things are changing...
Blockchain is near of
this point
11. BigML, Inc 11VSSML18: PITIA PROJECT.
But things are changing...
Smart Contract Auditing ICO Valoration
Deliver Onchain
Transactions
eHealth
Recomendation
eHealth
Scheduling
New uses cases to add new value to our clients
12. BigML, Inc 12VSSML18: PITIA PROJECT.
And ML is a catalyst
23
ML can help to enhance this
leap
We can create amazing things using ML
13. BigML, Inc 13VSSML18: PITIA PROJECT.
Not all tools are born equal
23
BigML brings ML for everyone
14. BigML, Inc 14Xxxxxx
An example: Pitia Project
(the Developer View)
ICO Valoration Project
17. BigML, Inc 17VSSML18: PITIA PROJECT.
An ICO is a new way to get money for a project using
cryptocuyrrencies in the process
Follow the money... (3/5)
21. BigML, Inc 21VSSML18: PITIA PROJECT.
And we want to predict the future, so......
Pitia
• Pitia is the name of the
priestess who ask questions
to Delphos Oracle
• The word pythoness has its
source in the word Pitia
22. BigML, Inc 22VSSML18: PITIA PROJECT.
Now
We are working in this áreas
(supervised model – decisions tree) Alexandria Integration
23. BigML, Inc 23VSSML18: PITIA PROJECT.
2
1 3
4
6
2018 2019
March
Dataset 200 ICOs
March
February
Project Launch
January
Pitia
project Starting
October
New UI
design for
Alexandria Web
December
Endpoints with
Alexandria
Clluc’s development path
Dataset
3000 ICOs 5
24. BigML, Inc 24VSSML18: PITIA PROJECT.
A working in progress (1/7)
• We are focus in data and what is important for our
problem (feature engineering)
• But we aren´t experts in feature engineering (not yet ;-))
and we are learning about it and how to improve our
work (R&D process)
• Recolecting information than more 3000 active ICOs and
the historic of all fail since 2016 and answering the
questions and making new ones.
• Many of them we are introducing by hand!!! A hard work!
• But we are creating our conectors for collecting data
using automatic tolos (we have to improve this)
• Our target: has an operative tool before March 2019
• And integrate into Alexandria our development framework
25. BigML, Inc 25VSSML18: PITIA PROJECT.
A working in progress (2/7)
• Alexandria is a REST framework to develop Blockchain
applications without understand Blockchain (supporting
now only Ethereum Blockchain)
• We are incorporating logic to work with Hyperledger
Private Blockchain
• And will incorporate our AI models